Search research articles

Search research articles

Related Experiment Video

Updated: Sep 21, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Multiple Traffic Target Tracking with Spatial-Temporal Affinity Network.

Yamin Sun^1,2, Yue Zhao³, Sirui Wang⁴

¹School of Architecture & Civil Engineering, Xi'an University of Science & Technology, Xi'an 710054, China.

Computational Intelligence and Neuroscience

|June 3, 2022

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study introduces a deep learning network for traffic target tracking, improving data association by learning spatial-temporal features. The proposed method achieves competitive performance on multiple datasets, enhancing autonomous driving systems.

Area of Science:

Computer Vision
Artificial Intelligence
Intelligent Transportation Systems

Background:

Multiple object tracking (MOT) is crucial for intelligent transportation systems and autonomous driving.
Current MOT methods often rely on handcrafted features for data association, limiting robustness.
Deep learning has advanced object detection but data association remains a challenge.

Purpose of the Study:

To develop a deep learning-based approach for robust spatial-temporal affinity learning in traffic target tracking.
To improve the data association step in multiple object tracking using an encoder-decoder network.

Main Methods:

A spatial-temporal encoder-decoder affinity network is proposed for multiple traffic target tracking.
A two-stage transformer encoder module captures image-level and tracklet-level features for spatial and temporal information.

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT

Published on: May 27, 2012

Related Experiment Videos

Last Updated: Sep 21, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT

Published on: May 27, 2012

A spatial transformer decoder computes association affinity using encoded features for online tracking.

Main Results:

The proposed method achieved competitive results on the KITTI, UA-DETRAC, and VisDrone datasets.
On KITTI, the method reached 86.9% MOTA and 85.71% MOTP, outperforming 15 SOTA methods.
Strong performance was also demonstrated on UA-DETRAC and VisDrone datasets, showing superior tracking capabilities.

Conclusions:

The proposed spatial-temporal encoder-decoder affinity network effectively learns robust features for data association.
The deep learning approach significantly enhances traffic target tracking performance compared to existing methods.
This work contributes to advancing scene understanding and autonomous driving capabilities in intelligent transportation systems.